Heart disease plots
df_heart_disease <- read.csv("heart_disease_full.csv", header = TRUE,
stringsAsFactors = TRUE)
#str(df_heart_disease)
df_heart_disease <- df_heart_disease %>%
mutate(stroke_risk = as.integer(hypertension | stroke))
#str(df_heart_disease)
df_heart_disease <- df_heart_disease %>% select(heart_disease, age,
avg_glucose_level, stroke_risk,
gender, work_type, smoking_status)
#str(df_heart_disease)
df_heart_disease <- df_heart_disease %>%
mutate(across(c(heart_disease, stroke_risk, gender,
work_type, smoking_status), as.factor))
#str(df_heart_disease)
#head(df_heart_disease)
df_heart_disease <- df_heart_disease %>%
mutate(
heart_disease = recode(heart_disease, `0` = "no heart disease",
`1` = "heart disease")
)
df_heart_disease <- df_heart_disease %>%
mutate(
stroke_risk = recode(stroke_risk, `0` = "no stroke risk",
`1` = "stroke risk")
)
#str(df_heart_disease)
levels(df_heart_disease$heart_disease)
## [1] "no heart disease" "heart disease"
levels(df_heart_disease$stroke_risk)
## [1] "no stroke risk" "stroke risk"
levels(df_heart_disease$gender)
## [1] "Female" "Male"
levels(df_heart_disease$work_type)
## [1] "Govt_job" "Private" "Self-employed"
levels(df_heart_disease$smoking_status)
## [1] "formerly smoked" "never smoked" "smokes" "Unknown"
df_heart_disease <- df_heart_disease %>%
mutate(
work_type = recode(work_type, 'Govt_job' = 'Govt job',
'Private' = 'Private',
'Self-employed' = 'Self-employed')
)
levels(df_heart_disease$work_type)
## [1] "Govt job" "Private" "Self-employed"
#str(df_heart_disease)
#risk in stroke risk means risk of heart disease (due to stroke)
#head(df_heart_disease)
palette <- scale_color_brewer(palette = "Dark2")
#Three variables in a plot:age, avg_glucose_level, and heart_disease
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level, color =
heart_disease)) +
geom_point() +
facet_wrap(~ heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Heart disease status"
) +
palette
#Four variables in a plot:age, avg_glucose_level,heart_disease, and gender
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = heart_disease)) +
geom_point() +
facet_grid(gender ~ heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Heart disease status"
) +
palette
##Four variables in a plot:age, avg_glucose_level,heart_disease, and work_type
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = heart_disease)) +
geom_point() +
facet_grid(work_type~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Heart disease status"
) +
palette
###Four variables in a plot:age, avg_glucose_level,heart_disease, and
### smoking_status
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = heart_disease)) +
geom_point() +
facet_grid(smoking_status~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Heart disease status"
) +
palette
#-----------------------
#Five variables in a plot: Age, avg_glucose_level, heart_disease,
# smoking status, and Gender
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = gender)) +
geom_point() +
facet_grid(smoking_status~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Gender"
) +
palette
#Five variables in a plot: Age, avg_glucose_level, heart_disease,
# smoking status, and Gender
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = gender)) +
geom_point() +
facet_grid(work_type~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Gender"
) +
palette
#Five variables in a plot: Age, avg_glucose_level, heart_disease,
# work_type, and stroke_risk
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = stroke_risk)) +
geom_point() +
facet_grid(work_type~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Stroke risk status"
) +
palette
#--------
#Note that Age is fixed on x axis, avg_glucose_level is fixed on y axis and
#heart_disease is doing the column-wise faceting and is also fixed. We can
#therefore only have four possible plots with six variables in a plot from
#all the variables under consideration which are:
#Numerical variables: age and avg_glucose_level (2)
#Nominal variables: gender, stroke risk, heart disease, work_type,
# smoking_status (5)
#We can obtain the four plots by dropping one at a time smoking_status,
#work_type, stroke_risk, and gender
#Six variables in a plot:Age, avg_glucose_level, heart_disease, gender,
# work_type, stroke_risk (dropping smoking status)
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = gender, shape = stroke_risk)) +
geom_point() +
facet_grid(work_type~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Gender",
shape = "Stroke risk"
) +
palette
#Six variables in a plot:Age, avg_glucose_level, heart_disease, gender,
# smoking_status, stroke_risk (dropping work type)
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = gender, shape = stroke_risk)) +
geom_point() +
facet_grid(smoking_status~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Gender",
shape = "Stroke risk"
) +
palette
#Six variables in a plot:Age, avg_glucose_level, heart_disease,
# smoking_status, stroke_risk (dropping gender)
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = work_type, shape = stroke_risk)) +
geom_point() +
facet_grid(smoking_status~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Work type",
shape = "Stroke risk"
) +
palette
#Six variables in a plot:Age, avg_glucose_level, heart_disease,
# smoking_status, gender (dropping stroke_risk)
ggplot(df_heart_disease, aes(x = age, y = avg_glucose_level,
color = gender, shape = work_type)) +
geom_point() +
facet_grid(smoking_status~heart_disease) +
labs(
x = "Age",
y = "Average Glucose Level",
color = "Gender",
shape = "Work type"
) +
palette
